In this paper, we investigate the problem of channel estimation in amplify-and-forward multiple-input multiple-output relaying systems operating over random wireless channels. Using the Bayesian framework, novel linear minimum mean square error and expectation-maximization based maximum a posteriori channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. Moreover, new, explicit expressions for the Bayesian Cramer-Rao bound are deduced for predicting and evaluating the channel estimation accuracy. Our simulation results demo nstrate that the incorporation of prior knowledge into the channel estimation algorithm offers significantly improved performance, especially in the low signal-to-noise ratio regime.

In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-to-noise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime.

In this paper, we consider the problem of channel estimation in multiple-input multiple-output (MIMO) amplifyand- forward (AF) relaying systems operating over time varying channels. Only data at the receiving end are assumed available for the estimation. By employing a first-order autoregressive (AR) model for characterizing the time-varying nature of the channels to be estimated, we derive an expectation-maximization (EM) Kalman filter (KF) that utilizes the received signal at the destination to track the individual channel links. The extended KF algorithm is also derived and compared to the proposed EM-based KF. Our simulation results show that the proposed EM-based KF offers better estimation performance with less complexity when compared to the EKF algorithm.

In this paper, we present a channel estimation scheme for Amplify-and-Forward (AF) relaying systems, using measurements at the destination only. A Least-Squares (LS) based channel estimation algorithm is developed, that provides the destination with full knowledge of all channel responses involved in the transmission. To investigate the algorithm performance, the Cramer-Rao lower bound (CRB) is analytically computed and compared with the asymptotic covariance of the proposed estimator. Since the existing estimator does not reach the CRB, we also propose and analyze an improved algorithm by taking into account the noise characteristics via weighted LS (WLS). The improved algorithm is asymptotically efficient, since it attains the CRB as the SNR tends to infinity.

Full-duplex relays can provide cost-effective cover-age extension and throughput enhancement. However, the main limiting factor is the resulting self-interference signal which deteriorates the relay performance. In this paper, we propose a novel technique for self-interference suppression in full-duplex Multiple-Input Multiple-Output (MIMO) relays. The relay employs transmit and receive weight filters for suppressing the self-interference signal. Unlike existing techniques that are based on zero forcing of self-interference, we aim at maximizing the ratio between the power of the useful signal to the self-interference power at the relay reception and transmission. Our simulation results show that the proposed algorithm outperforms the existing schemes since it can suppress interference substantially with less impact on the useful signal.